Expanding search queries

ABSTRACT

Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/578,100, filed Oct. 27, 2017, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

An embodiment of the invention relates generally to search queries and,more specifically, to expanding search queries.

BACKGROUND

Current web services enable users to access a large amount of data. Forexample, web services that provide job listings allow users to accessthousands of available job listings. As another example, a web servicethat provides member profiles allows a recruiter to access thousands ofcandidate profiles. While these types of web services provide a largeamount of available data, finding relevant data can be difficult. Toalleviate this issue, many systems provide search functionality thatallows users to formulate search queries to identify subsets of the datathat are pertinent to the requesting user. For example, these systemsmay allow a user to enter keywords as well as designate geographicallimitations to generate a search query. While these types of searchqueries allow a user to target their search for relevant information, insome instances they may result in only a few or even no results.Further, a user may be uncertain on how to properly broaden their searchto identify relevant data. From a user's perspective, this can be veryfrustrating, and may lead to the user abandoning use of the service.Accordingly, improvements are needed.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some embodiments are illustrated by way of example, and notlimitation, in the figures of the accompanying drawings in which:

FIG. 1 shows a system configuration, wherein electronic devicescommunicate via a network for purposes of exchanging data, according tosome example embodiments.

FIG. 2 is a block diagram of a search system, according to some exampleembodiments.

FIG. 3 is a flowchart showing an example method of executing a secondsearch query, according to certain example embodiments.

FIG. 4 is a flowchart showing an example method of determining whetherto modify the geographic indicator or the search term to execute asecond search query, according to certain example embodiments.

FIG. 5 is a block diagram of the geographic indicator expansion module,according to some example embodiments.

FIG. 6 is a block diagram of the search term expansion module, accordingto some example embodiments.

FIG. 7 is a block diagram of the search term expansion module, accordingto some example embodiments.

FIG. 8 illustrates a Siamese model, accordingly to some example,embodiments.

FIG. 9 is a flowchart showing an example method of determining anexpanded search term, according to certain example embodiments.

FIG. 10 is a flowchart showing an example method of determining anexpanded search term, according to certain example embodiments.

FIGS. 11A and 11B are examples of screenshots of search resultsgenerated from an expanded search query, according to some embodiments.

FIG. 12 is a block diagram illustrating a representative softwarearchitecture, which may he used in conjunction with various hardwarearchitectures herein described.

FIG. 13 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, variousdetails are set forth in order to provide a thorough understanding ofvarious embodiments of the invention. It will be apparent, however, toone skilled in the art, that the present subject matter may be practicedwithout these specific details, or with slight alterations.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure or characteristic describedin connection with the embodiment is included in at least one embodimentof the present subject matter. Thus, the appearances of the phrase “inone embodiment” or “in an embodiment” appearing in various placesthroughout the specification are not necessarily all referring to thesame embodiment.

For purposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the presentsubject matter. However, it will be apparent to one of ordinary skill inthe art that embodiments of the subject matter described may bepracticed without the specific details presented herein, or in variouscombinations, as described herein. Furthermore, well-known features maybe omitted or simplified in order not to obscure the describedembodiments. Various examples may be given throughout this description.These are merely descriptions of specific embodiments. The scope ormeaning of the claims is not limited to the examples given.

Disclosed are systems, methods, and non-transitory computer-readablemedia for expanding search queries. Current search systems enable usersto provide search parameters to identify relevant data. For example,search systems often enable users to enter a search term consisting ofone or more keywords, which the search system uses to execute a searchfor relevant data. Hence, a user searching for Italian restaurants mayenter a search term such as “Italian restaurants,” The search systemuses the search term (i.e., Italian restaurants) to identify relevantdata (e.g., webpages, restaurant listing) that has been tagged withand/or includes the search term or the individual keywords (i.e.,Italian, and restaurants), which are returned to the user as searchresults.

To further assist the user, some search systems enable users to setgeographic limitations for a search. For example, a user may enter ageographic indicator that describes a geographic region in which theuser would like to limit the search. Thus a user may provide the searchterm “Italian restaurant” and limit the search based on a geographicindicator, such as within a 10 mile radius of the user or,alternatively, within a selected city. The search system then executes asearch based on the provided search term and the provided geographicindicator. For example, the search system uses the search term (e.g.,Italian restaurants) and the geographic indicator (e.g., within 10miles) to identify relevant data (e.g., webpages, restaurant listing)that have been tagged with and/or include the search term or theindividual keywords (e.g., Italian, and restaurants), and are associatedwith location data that is within the selected geographic indicator. Asa result, the user will be presented with search results that of Italianrestaurants within the designated geographic location.

While using search terms and geographic indicators results in relevantsearch results, it also greatly reduces the number of search resultsthat a user receives. For example, there are many more Italianrestaurants than those within 10 miles of a user. Likewise, there aremany more restaurants than just Italian restaurants. In some cases, theuse of search terms and geographic indicators may result in very few oreven no search results being returned. In this type of scenario, usershave traditionally been tasked with adjusting their chosen search termand/or geographic indicator, which requires a user to guess how tomodify each to receive the search results they desire.

To alleviate this issue, a search system consistent with someembodiments of the invention is configured to execute a second searchquery when the number of search results from a first search query areless than a specified threshold number. Further, the search systemdetermines how to modify the original search query to receive additionalsearch results. That is, the search system determines whether to expandthe search term or the geographic indicator, and then executes thesecond search based on the expanded search term or expanded geographicindicator. For example, if a search query for “Italian restaurants”within 10 miles results in a low number of search results, the searchsystem determines whether to expand the search term (i.e., ItalianRestaurant) or the geographic indicator (i.e., within 10 miles), andthen executes a second search query. As a result, the search system mayexecute a second search query for Italian restaurants within 20 miles(i.e., expanded geographic indicator), or a second search query forItalian or Greek restaurants (i.e., expanded search term) within 10miles.

The search system determines whether to expand the search term or thegeographic indicator based on historical search logs of other users. Thehistorical search logs include records for previously submitted searchqueries. Each record includes the search term, geographic indicator andtime stamp of the search query. The search system uses the historicalsearch logs to calculate a likelihood value that indicates thelikelihood that other users in the same geographic area would expand thegeographic indicator. The search system then compares the likelihoodvalue to a threshold likelihood value and determines to increase thegeographic indicator if the likelihood value meets or exceeds thethreshold likelihood value. Alternatively, the search system determinesto increase the search term when the likelihood value is less than thethreshold likelihood value. Other aspects of the various embodiments ofthe invention will be readily apparent from the description of thefigures that follows.

FIG. 1 shows an example system configuration 100, wherein electronicdevices communicate via a network for purposes of exchanging data,according to some example embodiments. As shown, multiple devices (i.e.,a client device 102 and a search system 104) are connected to acommunication network 104 and configured to communicate with each otherthrough use of the communication network 104. The communication network104 is any type of network, including a local area network (“LAN”), suchas an intranet, a wide area network (“WAN”), such as the Internet, orany combination thereof. Further, the communication network 104 may be apublic network, a private network, or a combination thereof. Thecommunication network 104 is implemented using any number ofcommunication links associated with one or more service providers,including one or more wired communication links, one or more wirelesscommunication links, or any combination thereof. Additionally, thecommunication network 104 is configured to support the transmission ofdata formatted using any number of protocols.

Multiple computing devices can be connected to the communication network104. A computing device is any type of general computing device capableof network communication with other computing devices. For example, acomputing device can be a personal computing device such as a desktop orworkstation, a business server, or a portable computing device, such asa laptop, smart phone, or a tablet Personal Computer (PC). A computingdevice can include some or all of the features, components, andperipherals of the machine 1300 shown in FIG. 13.

To facilitate communication with other computing devices, a computingdevice includes a communication interface configured to receive acommunication, such as a request, data, etc., from another computingdevice in network communication with the computing device and pass thecommunication along to an appropriate processing module executing on thecomputing device. The communication interface also sends a communication(e.g., transmits data) to other computing devices in networkcommunication with the computing device.

In the system 100, users interact with the search system 104 to executesearch queries for data. For example, a user uses the client device 102connected to the communication network 106 by direct and/or indirectcommunication to communicate with and utilize the functionality of thesearch system 104. Although the shown system 100 includes only oneclient device 102, this is only for ease of explanation and is not meantto be limiting. One skilled in the art would appreciate that the system100 can include any number of client devices 102. Further, the searchsystem 104 may concurrently accept connections from and interact withany number of client devices 102. The search system 104 supportsconnections from a variety of different types of client devices 102,such as desktop computers; mobile computers; mobile communicationdevices, e.g, mobile phones, smart phones, tablets; smart televisions;set-top boxes; and/or any other network-enabled computing devices.Hence, the client device 102 may be of varying type, capabilities,operating systems, etc.

A user interacts with the search system 104 via a client-sideapplication installed on and executing at the client device 102. In someembodiments, the client-side application includes a search systemspecific component. For example, the component may be a stand-aloneapplication, one or more application plug-ins, and/or a browserextension. However, the users may also interact with the search system104 via a third-party application, such as a web browser, that resideson the client device 102 and is configured to communicate with thesearch system 104. In either case, the client-side application presentsa user interface (UI) for the user to interact with the search system104. For example, the user interacts with the search system 104 via aclient-side application integrated with the file system or via a webpagedisplayed using a web browser application.

The search system 104 comprises one or more computing devices configuredto execute user specified search queries for data and provide anyresulting search results to the user. The search system 104 can be astandalone system or integrated into other systems or services, such asbeing integrated into a website, web service, etc. For example, thesearch system 104 may be integrated into a professional socialnetworking service and used to facilitate search queries for jobpostings maintained by the professional social networking service. Ineither case, the search system 104 facilitates search queries for data,where a user using a client device 102 can enter search parameters forthe search query and receive any resulting search results.

The search system 104 enables a user to execute a search query for datamaintained by the search system 104 and/or data maintained by other datasources (not shown) in network communication with the search system 104.For example, the search system 104 provides the user with a searchinterface that enables the user to provide search parameters, such as asearch term and geographic indicator. A search term comprises one ormore keywords provided by the user. The geographic indicator defines ageographic area to which a user would like search results limited. Forexample, he geographic indicator may be defined by a radius (e.g.,within 10 miles), or by a geographic region (e.g., SanJose).Accordingly, a user uses the geographic indicator to indicate thatthey would like search results that are associated with a geographiclocation that is within the geographic region.

In response to receiving a search term and a geographic indicator from aclient device 102, the search system 104 executes a search query basedon the search term and geographic indicator. For example, the searchsystem 104 searches data in a data storage maintained by the searchsystem 104 and/or web service in which the search system 104 isintegrated. The search system 104 may also search data stored by otherdata sources. The search system 104 provides any resulting searchresults to the client device 102, where they are presented to therequesting user.

In instances where the search query results in few search results, thesearch system 104 executes a second search query based on an expandedsearch term or expanded geographic indicator. For example, if the searchsystem 104 determines that a number of resulting search results is lessthan a threshold number of search results, the search systemautomatically executes a second search query based on either an expandedsearch term or expanded geographic indicator to provide the user with agreater number of search results.

The search system 104 determines whether to expand the search term orthe geographic indicator, and then executes the second search based onthe expanded search term or expanded geographic indicator. For example,if a search query for “Italian restaurants” within 10 miles results in alow number of search results, the search system 104 determines whetherto expand the search term Italian Restaurant) or the geographicindicator (i.e., within 10 miles), and then executes a second searchquery. As a result, the search system 104 may execute a second searchquery for Italian restaurants within 20 miles (i.e., expanded geographicindicator), or a second search query for Italian or Greek restaurants(i.e., expanded search term) within 10 miles.

The search system 104 determines whether to expand the search term orthe geographic indicator based on historical search logs of other users.The historical search logs include records for previously submittedsearch queries facilitated by the search system 104. Each recordincludes the search term, geographic indicator and time stamp of thesearch query. The search system 104 uses the historical search logs tocalculate a likelihood value that indicates the likelihood that otherusers in the same geographic area would expand the geographic indicator.The search system 104 then compares the likelihood value to a thresholdlikelihood value and determines to increase the geographic indicator ifthe likelihood value meets or exceeds the threshold likelihood value.Alternatively, the search system 104 determines to increase the searchterm when the likelihood value is less than the threshold likelihoodvalue.

FIG. 2 is a block diagram of the search system 104, according to someexample embodiments. To avoid obscuring the inventive subject matterwith unnecessary detail, various functional components (e.g., modules)that are not germane to conveying an understanding of the inventivesubject matter have been omitted from FIG. 2. However, a skilled artisanwill readily recognize that various additional functional components maybe supported by the search system 104 to facilitate additionalfunctionality that is not specifically described herein. Furthermore,the various functional modules depicted in FIG. 2 may reside on a singlecomputing device or may be distributed across several computing devicesin various arrangements such as those used in cloud-based architectures.

As shown, the messaging system 106 includes an interface module 202, asearch query module 204, a sufficient search result determination module206, an expansion determination module 208, a geographic indicatorexpansion module 210, a search term expansion module 212, and a datastorage 214.

The interface module 202 provides a user's client device 102 with asearch interface that enables the user to execute a search query fordata as well as review the corresponding search results. For example,the interface module 202 provides data the user's client device 102,that the user's client device 102 uses to provide the search interface.Similarly, the interface module 202 receives data from the user's clientdevice 102 to provide the functionality of the search interface.

The search interface includes user interface elements, such as buttons,text, boxes, drop down boxes, etc., that enable a user to enter searchparameters to execute a search. The search parameters include a searchterm consisting of one or more keywords, and a geographic indicator thatidentifies a geographic area to which the user would like to limit thesearch results. This means that the user would like to receive searchresults that are associated with a geographic location that is withinthe geographic area defined by the geographic indicator. In addition toenabling the user to input search parameters, the search interface alsopresents the user with any search results. For example, the searchinterface lists the search results and enables the user to select,click, etc., the search results to access secondary informationassociated with the search result. For example, the search resultsinclude the titles of jobs identified as a result of the user's providedsearch parameters. The user may select one of the search results toaccess additional details about a selected job listing.

The search query module 204 executes a search query based on the searchparameters provided by the user search term and geographic indicator).For example, the search query module 204 executes a search in one ormore data stores for data that includes the search term and/or theindividual keyword of the search term, and includes data indicating thatthe data is associated with the geographic area specified by thegeographic indicator. For example, the search query module 204 executesa search query of a data store including data describing restaurants fordata that include the search term provided by the user (e.g., Italianrestaurant), and includes data indicating that the restaurant is locatedwithin the geographic area. defined by the geographic indicator (e.g.,within 10 miles). As another example, the search query module 204executes a search query of a data store including data describing joblistings for data that includes the search term provided by the user(e.g., dishwasher), and included data indicating that the job is withinthe geographic are defined by the geographic indicator (e.g., Nome,Ak.).

The search query module 204 may execute the search query in the datastorage 214 maintained by the search system 104 or a service in whichthe search system is implemented (e.g., a professional social networkingservice). Alternatively, the search query module 204 may execute thesearch query in data stores maintained by web servers, web services,etc., that are in network connection with the search query module 204.The search query module 204 returns any search results of the searchquery.

The search query module 204 also creates a record of each executedsearch query. The data storage 214 maintains historical search logsincluding records of each executed search. The search query module 204updates the historical search logs in the data storage 214 to recordeach executed search query. Each record in the historical search logsincludes data describing the search query, such as the search parametersused (i.e., search term and geographic indicator), the timestampassociates with the search query (i.e., the time the search query wasexecuted), a device identifier for the client device 102 that requestedthe search query, etc. The historical search logs also include dataindicating the search results provided to the user, as well as whichsearch results, if any, the user selected.

The sufficient search result determination module 206, determineswhether an executed search query resulted in a sufficient number ofsearch results to be presented to the user, or if an additional searchshould be executed based on expanded search parameters expanded searchterm or expanded geographic indicator) to gather additional searchresults. To accomplish this, the sufficient search results determinationmodule 206 determines the number of search results that resulted from asearch query and compares the number of search results to a thresholdnumber of search results. If the number of search results meets orexceeds the threshold number of search results, the sufficient searchresult determination module 206 determines that the search queryresulted in a sufficient number of search result to be presented to theuser, and the interface module 202 presents the search results on theuser's client device 102. Alternatively, if the number of search resultsis less than the threshold number of search results (i.e., the number ofsearch results does not meet or exceed the threshold number of searchresults), the sufficient search result determination module 206determines that the search query did not result in a sufficient numberof search results to present to the user. As a result, an additionalsearch will be executed based on expanded search parameters expandedsearch term or expanded geographic indicator) to gather additionalsearch results.

The expansion determination module 208, determines which searchparameter should be expanded. That is, the expansion determinationmodule 208 determines whether the search term or the geographicindicator should be expanded to execute a second search query. This isperformed when the sufficient search result determination module 206determines that the search query did not result in a sufficient numberof search results to present to the user. The expansion determinationmodule 208 determines whether to expand the search parameter or thegeographic indicator based on the historical search logs. For example,the expansion determination module 208 uses the historical search logsto determine whether other users that executed searches in the samegeographic area chose to expand the search term or the geographicindicator when manually entering search parameters for a second search.The expansion determination module 208 uses this data to calculate alikelihood value that indicates a likelihood that users in thegeographic area will expand the geographic indicator when executing asecond search. The expansion determination module 208 then compares thelikelihood value to a threshold likelihood value. If the likelihoodvalue meets or exceeds the threshold likelihood value, the expansiondetermination module 208 determines that the geographic indicator shouldbe expanded for the second search. Alternatively, if the likelihoodvalue is less than the threshold likelihood value (i.e., the likelihoodvalue does not meet or exceed the threshold likelihood value), theexpansion determination module 208 determines that the search termshould be expanded for the second search. The expansion determinationmodule 208 may perform these operations at one time based on a varietyof geographic areas and store the results for later user. Alternatively,the expansion determination module 208 may perform these operations inresponse to a search query that resulted in a low number of searchresults.

In some embodiments, the expansion determination module 208 uses thehistorical search logs to generate a statistical model. The statisticalmodel outputs a likelihood value based on a given input geographiclocation. Accordingly, the expansion determination module 208 uses thestatistical model to determine the likelihood value.

The geographic indicator expansion module 210 generates an expandedgeographic indicator for a second search query. For example, in responseto the expansion determination module 208 determining that thegeographic indicator should be expanded, the expansion determinationmodule 208 provides the geographic indicator to the geographic indicatorexpansion module 210 and instructs the geographic indicator expansionmodule 210 to generate an expanded geographic indicator.

The expanded geographic indicator will define an expanded geographicarea that is larger than the geographic area defined by the originalgeographic indicator. The expanded geographic area defined by theexpanded geographic indicator may encompass the geographic area definedby the original geographic indicator, meaning that the expandedgeographic area will include the original geographic area as well asother geographic areas not included in the original geographic area. Forexample, an expanded geographic indicator may increase the radius of theoriginal geographic indicator (e.g., increase the radius from within 10miles to within 15 miles). As another example, the expanded geographicarea may increase the geographic region of the original geographic area(e.g., increase the geographic area from the city of San Francisco tothe San Francisco bay area).

In either case, the geographic indicator expansion module 208 providesthe expanded geographic indicator to the search query module 204. Thesearch query module 204 then executes a second search based on theoriginal search term and the expanded geographic indicator. Thecorresponding search results are presented to the user by the interfacemodule 202. The search results may be presented with an indication thatthey include search results gathered using an expanded geographicindicator. The functionality of the geographic indicator expansionmodule 208 is explained in greater detail below in relation to FIG. 5.

The search term expansion module 212 generates an expanded search termfor a second search query. For example, in response to the expansiondetermination module 208 determining that the search term should beexpanded, the expansion determination module 208 provides the searchterm to the search term expansion module 212 and instructs the searchterm expansion module 212 to generate an expanded search term.

The expanded search term will broaden the search by, for example, addingor substituting alternate keywords or, alternatively, removing thelimitations of some of the keywords in the search term. For example, thesearch term expansion module 212 can broaden the search term “Italianrestaurant” by adding or substituting the keyword “Greek.” As a result,the second search query will identify Greek restaurants in addition tothe Italian restaurants identified in the first search. As anotherexample of adding search terms, the search term expansion module 212broadens the search term “database engineer Apple” by substituting thekeyword “Google.” As a result, the second search query will identifydatabase engineering roles at Google in addition to database engineerpositions at Apple identified in the first search.

The search term expansion module 212 may also broaden a search term byremoving keywords to broaden the scope of the search. For example, giventhe original search term “database engineer Apple,” the search termexpansion module 212 may remove the keyword “database,” resulting in thebroader search term “engineer Apple.” As a result, the second searchwill provide search results for a broader scope of engineering positionsat Apple, such as software engineers, systems engineer, etc., as well asdatabase engineers.

The search term expansion module 210 provides the expanded search termto the search query module 204. The search query module 204 thenexecutes a second search based on the original geographic indicator andthe expanded search term. The corresponding search results are presentedto the user by the interface module 202. The search results may bepresented with an indication that they include search results gatheredusing an expanded search term. The functionality of the search termexpansion module 210 is explained in greater detail below in relation toFIGS. 6 and 7.

FIG. 3 is a flowchart showing an example method 300 of executing asecond search query, according to certain example embodiments. Themethod 300 may be embodied in computer readable instructions forexecution by one or more processors such that the operations of themethod 300 may be performed in part or in whole by the search system104; accordingly, the method 300 is described below by way of examplewith reference thereto. However, it shall be appreciated that at leastsome of the operations of the method 300 may be deployed on variousother hardware configurations and the method 300 is not intended to belimited to the search system. 104.

At operation 302, the interface module 202 receives search parametersincluding a search term and a geographic indicator. The interface module202 provides a user's client device 102 with a search interface thatenables the user to execute a search query for data as well as reviewthe corresponding search results. For example, the search interfaceincludes user interface elements, such as buttons, text, boxes, dropdown boxes, etc., that enable a user to enter search parameter toexecute a search. The search parameters include a search term consistingof one or more keywords, and a geographic indicator that describes ageographic area. The user uses their client device 102 to enter thesearch parameters, which are then transmitted to the search system 104.

At operation 304, the search query module 204 executes a search querybased on the search parameters. For example, the search query module 204executes a search of one or more data stores for data that includes thesearch term and/or the individual keyword(s) of the search term, andincludes data indicating that the data is associated with the geographicarea specified by the geographic indicator. For example, the searchquery module 204 executes a search query of a data store including datadescribing restaurants for data that includes the search term providedby the user (e.g., Italian restaurant), and includes data indicatingthat the restaurant is located within the geographic area defined by thegeographic indicator (e.g., within 10 miles). As another example, thesearch query module 204 executes a search query of a data storeincluding data describing job listing for data that includes the searchterm provided by the user (e.g., dishwasher), and included dataindicating that the job is within the geographic are defined by thegeographic indicator (e.g., Nome, Ak.).

The search query module 204 may execute the search query in the datastorage 214 maintained by the search system 104 or a service in whichthe search system is implemented (e.g., a professional social networkingservice). Alternatively, the search query module 204 may execute thesearch query in data stores maintained by web servers, web services,etc., that are in network connection with the search query module 204.The search query module 204 returns any search results of the searchquery,

At operation 306, the sufficient search result determination module 206determines whether a number of search results returned as a result ofthe search query is sufficient to present to the user, or if anadditional search should be executed based on expanded search parameters(i.e., expanded search term or expanded geographic indicator) to gatheradditional search results. To accomplish this, the sufficient searchresult determination module 206 determines the number of search resultsthat resulted from a search query and compares the number of searchresults to a threshold number of search results. If the number of searchresults meets or exceeds the threshold number of search results, thesufficient search result determination module 206 determines that thesearch query resulted in a sufficient number of search result to bepresented to the user, and at operation 312, the interface module 202presents the search results to the user. For example, the interfacemodule 202 causes the search results to be presented on the user'sclient device 102.

Alternatively, if the number of search results is less than thethreshold number of search results (i.e., the number of search resultsdoes not meet or exceed the threshold number of search results), thesufficient search result determination module 206 determines that thesearch query did not result in a sufficient number of search results topresent to the user. As a result, at operation 308, the search system104 generates expanded search parameters by expanding either the searchterm or the geographic indicator. To accomplish this, the expansiondetermination module 208 determines whether to expand the search term orthe geographic indicator. Once the determination is made, either thegeographic indicator expansion module 210 or the search term expansionmodule 212 generates an expanded geographic indicator or expanded searchterm respectively. The expanded geographic indicator or expanded searchterm are combined with the other original search parameters, resultingin the expanded search parameters. This operation is discussed ingreater detail in relation to FIG. 4.

At operation 310, the search query module 204 executes a second searchquery based on the expanded search parameters, and at operation 312, theinterface module 202 presents the search results to the user. The searchresults presented to the user include both the original search resultsof the search query executed based on the search parameters receivedfrom the client device 102, and the search results of the second searchquery executed based on the expanded search parameters.

FIG. 4 is a flowchart showing an example method 400 of generatingexpanded search parameters by expanding either the search term or thegeographic indicator, according to certain example embodiments. Themethod 400 may be embodied in computer readable instructions forexecution by one or more processors such that the operations of themethod 400 may be performed in part or in whole by the search system104; accordingly, the method 400 is described below by way of examplewith reference thereto. However, it shall be appreciated that at leastsome of the operations of the method 400 may be deployed on variousother hardware configurations and the method 400 is not intended to belimited to the search system 104.

At operation 402, the expansion determination module 208 determines alikelihood value based on the geographic area identified by thegeographic indicator. The likelihood value that indicates a likelihoodthat users in the geographic area will expand the geographic indicatorwhen executing a second search. The expansion determination module 208uses historical search logs to determine whether other users thatexecuted searches in the same geographic area chose to expand the searchterm or the geographic indicator when manually entering searchparameters for a second search. The expansion determination module 208then uses this data to calculate the likelihood value.

In some embodiments, the expansion determination module 208 uses thehistorical search logs to generate a statistical model. The statisticalmodel outputs a likelihood value based on a given input geographiclocation. Accordingly, the expansion determination module 208 uses thestatistical model to determine the likelihood value.

At operation 404, the expansion determination module 208 compares thelikelihood value to a threshold likelihood value, and at operation 406,the expansion determination module 208 determines whether the likelihoodvalue meets or exceeds the threshold value. If the likelihood valuemeets or exceeds the threshold likelihood value, the expansiondetermination module 208 determines that the geographic indicator shouldbe expanded for the second search, and at operation 408, the geographicindicator expansion module 210 generates an expanded geographicindicator. Alternatively, if the likelihood value is less than thethreshold likelihood value (i.e., the likelihood value does not meet orexceed the threshold likelihood value), the expansion determinationmodule 208 determines that the search term should be expanded for thesecond search, and at operation 410, the search term expansion module212 generates an expanded search term.

At operation 412, the search query module 204 generates expanded searchparameters. The search query module 204 uses either the expanded searchterm or the expanded geographic indicator with the original searchparameters to generate the expanded search parameters. For example, thesearch query module 204 generates the expanded search parameters bycombining the expanded geographic indicator with the original searchterm. Alternatively, the search query module 204 generates the expandedsearch parameters by combining the expanded search term with theoriginal geographic indicator.

FIG. 5 is a block diagram of the geographic indicator expansion module210 according to some example embodiments. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents (e.g., modules) that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 5. However, a skilled artisan will readily recognize that variousadditional functional components may be supported by the geographicindicator module 210 to facilitate additional functionality that is notspecifically described herein. Furthermore, the various functionalmodules depicted in FIG. 5 may reside on a single computing device ormay be distributed across several computing devices in variousarrangements such as those used in cloud-based architectures.

The geographic indicator expansion module 210 generates an expandedgeographic indicator based on a given geographic indicator. For example,the geographic indicator expansion module 210 generates an expandedgeographic indicator based on the geographic indicator provided by theuser as search parameters. The geographic indicator expansion module 210may expand a geographic indicator using either radial expansion orregional expansion. Radial expansion includes expanding a geographicindicator by expanding a radius by a predetermined distance. Forexample, using radial expansion, a given radius of 5 miles may beexpanded to 10 miles. In contrast, the region expansion module 504expands a geographic region by expanding the geographic region. Forexample, using radial expansion, a given geographic region such as thecity of San Francisco may be expanded to the geographic region of theSan Francisco bay area, which includes the city of San Francisco as wellas the surrounding areas (i.e. Oakland, Palo Alto, San Jose, etc.).

The geographic indicator expansion module 210 can be configured to usereither expansion technique (i.e., radial expansion or region expansion),or to select which expansion technique to use based on one or morefactors. For example, the geographic indicator expansion module 210 mayselect which expansion technique to use based on the geographicindicator that is provided. For instance, if the geographic indicator isa radius, the geographic expansion module 210 may select to use radialexpansion to generate the expanded geographic indicator. Alternatively,if the geographic indicator is a geographic region, such as a city,state, etc., the geographic expansion module 210 may select to useregion expansion to generate the expanded geographic indicator.

As shown, the geographic indicator expansion module 210 includes aradial expansion module 502 and a region expansion module 504. Theradial expansion module 502 generates an expanded geographic indicatorusing the radial expansion technique. For example, given an initialgeographic indicator (e.g., within 10 miles), the radial expansionmodule 502 generates an expanded geographic indicator (e.g., within 20miles).

In some implementations, the radial expansion module 502 simply expandsthe geographic indicator by a predetermined distance. For example, theradial expansion module 502 expands the geographic indicator by 10miles. Thus, given a geographic indicator of a 10 mile radius, theradial expansion module 502 generates an expanded geographic indicatorof a 20 mile radius.

In some embodiments, the distance by which the radial expansion module502 expands a geographic indicator is based on the population density ofthe geographic location. For example, the radial expansion module 502expands a geographic indicator by a smaller distance when the geographiclocation has a higher population density, such as cities. Alternatively,the radial expansion module 502 expands a geographic indicator by alarger distance when the geographic location has a lower populationdensity, such as a rural area. This is because increasing the radius bya small margin in a city will most likely result in more search resultsbeing returned than in a rural area. For example, expanding a radius by1 mile in New York City may result in a high number of new results,whereas in extremely rural areas it may require expanding a radius by 30miles to achieve the same number of search results. Further, people inrural areas may be more willing to travel to a destination than peoplein densely populated areas, such as cities.

The region expansion module 504 expands a geographic indicator using aregional expansion technique. This includes expanding a geographicindicator based on a geographic region rather than a set radius. Theregion expansion module 504 maintains a hierarchal listing of geographicregions that is used to identify an expanded geographic region for agiven geographic region. For example, the hierarchal listing includes alisting of cities and towns and indicates a greater geographic regionthat each city and town falls within. Similarly, each greater geographicregion may be listed as being a part of an even larger geographicregion. For example, the cities San Francisco, Oakland and San Jose maybe listed as being a part of the greater San Francisco Bay Area. The SanFrancisco Bay Area, along with the Sacramento Area may be listed asbeing a part of Norther California, and Northern California along withSouthern California may be listed as being a part of the greatergeographic region of the State of California.

The region expansion module 504 searches the hierarchical listing ofgeographic regions based on a received geographic indicator, and thenidentifies its greater geographic region, which is used to generate theexpanded geographic indicator. For example, given a geographic indicatorof the city of San Francisco, the region expansion module 504 searchesthe hierarchical listing of geographic regions to identify that greatergeographic region to which the city of San Francisco falls within (e.g.,San Francisco Bay Area, Northern California, California, etc.). Theregion expansion module 504 then returns an expanded geographicindicator that identifies the greater geographic region (e.g., SanFrancisco Bay Area, Northern California, California, etc.).

FIG. 6 is a block diagram of the search term expansion module 212according to some example embodiments. To avoid obscuring the inventivesubject matter with unnecessary detail, various functional components(e.g., modules) that are not germane to conveying an understanding ofthe inventive subject matter have been omitted from FIG. 6. However, askilled artisan will readily recognize that various additionalfunctional components may be supported by the search term expansionmodule 212 to facilitate additional functionality that is notspecifically described herein. Furthermore, the various functionalmodules depicted in FIG. 6 may reside on a single computing device ormay be distributed across several computing devices in variousarrangements such as those used in cloud-based architectures

The search term expansion module 212 generates an expanded search termbased on a given search term. In some implementations, the search termexpansion module 212 generates an alternate keyword to supplement orreplace an original keyword in the search term. For example, a searchterm provided by a user to identify a job listing may indicate a desiredcompany (e.g., Apple), as well as a desired position (e.g., softwareengineer). To generate additional search results for a user, the searchterm expansion module 212 identifies an alternate company (e.g., Google)to include in the search term, thereby providing a user with additionalsearch results of system engineer positions at both Google and Apple.For example, the alternate company name (e.g., Google) may be used in asecond search query in place of the original company name (e.g., Apple).Alternatively, the alternate company name (e.g., Google) may be used inthe second search query along with the original company name (e.g.,Apple).

The search term expansion module 212 identifies an alternate companybased on calculated peer scores that indicate a probability of employeestransitioning between companies. For example, a peer score calculatedfor Apple to Google indicates a probability that employees of Applewould transition to working at Google. Likewise, a peer score calculatedfor Apple to Facebook indicates a probability that employees of Applewould transition to working at Facebook.

When given a search term that includes a keyword identifying a targetcompany, the search term expansion module 212 identifies alternatecompanies that have high peer scores with the target company. The searchterm expansion module 212 then selects an alternate company, which isused to generate the expanded search.

The search term expansion module 212 calculates the peer scores based onhistorical movement data that indicates employee transitions betweencompanies. The historical movement data is gathered from a professionalsocial networking service, such as Linkedln, that maintains user workhistory for multiple user accounts. For example the search termexpansion module 212 communicates with the professional socialnetworking service or, alternatively, may be implemented as part of theprofessional social networking services. The search term expansionmodule 212 gathers the historical movement data and calculates the peerscores, which are then used to select an alternate company.

As shown, the search term expansion module 212 includes a keywordidentification module 602, a data gathering module 604, a peer scorecalculation module 606, an alternate company selection module 608 and anexpanded search term generation module 610. The keyword identificationmodule 602 identifies one or more keywords in a search term thatidentify a company. To accomplish this the keyword identification module602 uses a listing of known company names and searches the listing basedon the keywords in the search term to identify a match.

The data gathering module 604 gathers historical movement data.Historical movement data is data that indicates employee transitionsfrom company to company. For example, historical movement data includesa user's work history that identifies the companies where the user hasworked, including the dates that the user started and ended employmentat each company. Historical movement data is available on professionalsocial networking services, such as LinkedIn, where users post thistheir work history to their user accounts.

In some implementations, the search system 104 is implemented as part ofa professional social networking service and thus the data gatheringmodule 604 gathers the historical movement data from local data storesmaintained by the professional social networking service. Alternatively,in embodiments in which the search system 104 is separate from theprofessional social networking service, the data gathering module 604communicates with computer servers that facilitate the social networkingservice to gather the historical movement data.

The historical transition data may include pairs of company identifiersto identify employee transitions from one company to another company.For example, a first company identifier in the pair of companyidentifiers identities a company that the user previously worked at, andthe second company identifier in the pair of company identifiersidentifies the company to which the user transitioned.

The peer score calculation module 606 uses the gathered historicalmovement data to calculate peer scores indicating the probability thatemployees will transition between companies. For example, to calculatethe peer score indicating the probability that employees will transitionfrom a target company to a second company, the peer score calculationmodule 606 determines, from the gathered historical movement data, thenumber of employee transitions from the target company to the secondcompany, the number of employee transitions from the target company toany other company, the number of employee transitions from the secondcompany to the target company, the number of employee transitions fromthe second company to any other company, etc. The peer score calculationmodule 606 then uses this gathered data to calculate the peer scoreindicating the probability that employees will transition from thetarget company to the second company.

The peer score calculation module 606 calculates the peer scores usingany of a variety of algorithms or techniques. For example, the peerscore calculation module 606 may generate a statistical model based onthe historical movement data that outputs peer scores given an inputcompany. The peer score calculation module 606 uses the generatedstatistical model to determine peer scores for a given company. Forexample, the peer score calculation module 606 uses a company nameidentified in a search term as input in the statistical model, resultingin a set of peer scores indicating the probability of employees of thecompany transitioning to other companies.

As another example, the peer score calculation module 606 calculates thepeer scores by generating vectors representing the companies based onthe historical movement data. The peer score calculation module 606 thendetermines the distance between the resulting vectors to determine thepeer score between companies. For example, companies with vectors thatare closer together are assigned higher peer scores, whereas companieswith vectors that are farther apart are assigned lower peer scores.

In some embodiments, the peer score calculation module 606 calculatesthe peer scores for two companies based on the number of employees thattransitioned between the companies and the total number of employeesthat transitioned from the company to other companies. For example, thepeer score calculation module 606 calculates the peer score betweencompany u and company v by determining the number of employees thattransitioned from company u to company v, as well as the number ofemployees that transitioned from company u to all other companies. Thepeer score calculation module 606 then divides the number of employeesthat transitioned from company u to company v by the number of employeesthat transitioned from company u to all other companies, yielding thepeer score. The peer score indicates the probability of an employeetransitioning from company u to company v.

As another example, the peer score calculation module 604 determines thepeer score between company u and company v using the followingalgorithm:

peer score(u, v):=P(c ₁ =u|c ₀ =v)/max_(w) P(c ₁ =w|c ₀ =v)·P(c ₁ =v|c ₀=u)/max_(w) P(c ₁ =w|c ₀ =u)

where P(c₁=v|c₀=u) is the probability of transitioning to company vgiven currently in company u.

These are only a few examples of how the peer score calculation module606 may calculate a peer score and is not meant to be limiting. The peerscore calculation module 606 may use any of a number of techniques andalgorithms to calculate a peer score, and this disclosure anticipatesall such embodiments.

The alternate company selection module 608 selects an alternate companybased on the calculated peer scores. For example, the alternate companyselection module 608 selects the alternate company with the highest peerscore with the company identified in the search term. Alternatively, thealternate company selection module 608 selects an alternate company thathas a peer score that meets or exceeds a threshold peer score. Forexample, the alternate company selection module 608 identifies all thecompanies that have a peer score above a threshold peer score and thenselects one of the companies as the alternate company. This can beperformed using any of a variety of techniques, such as by random,alphabetical, etc.

These are only a couple of examples of how the alternate companyselection module 608 selects an alternate company based on thecalculated peer scores and is not meant to be limiting. The alternatecompany selection module 608 may use any of a number of techniques andalgorithms to selects an alternate company based on the calculated peerscores, and this disclosure anticipates all such embodiments.

The expanded search term generation module 610 generates an expandedsearch term based on the alternate company. In some implementations, theexpanded search term generation module 610 generates the expanded searchterm by adding one or more new keywords to the original search term. Theadded keywords identify the alternate company. For instance, if theoriginal search term was “Software Engineer Apple,” the expanded searchterm generation module 610 may add the alternate company name (e.g.Google) to the search term as a new keyword. As a result, the expandedsearch term would comprise “Software Engineer Apple Google.” A searchquery based on this expanded search term would return results forsoftware engineer positions at both Apple and Google, thereby providinga user with additional search results.

In other implementations, the expanded search term generation module 610generates the expanded search term by replacing one or more originalkeywords with one or more new keywords that identify the alternatecompany. For example, given the original search term “Software EngineerApple,” the expanded search term generation module 610 replaces thekeyword ‘Apple’ with a keyword. identifying the alternate company (e.g.,Google). As a result, the expanded search term would comprise “SoftwareEngineer Google.” A search query based on this expanded search termwould return results for software engineer positions at Google. Thesesearch results would then be combined with search results based on theoriginal search term of “Software Engineer Apple,” thereby providing auser with additional search results for software engineer positions atboth Apple and Google.

FIG. 7 is a block diagram of the search term expansion module 212according to some example embodiments. To avoid obscuring the inventivesubject matter with unnecessary detail, various functional components(e.g., modules) that are not germane to conveying an understanding ofthe inventive subject matter have been omitted from FIG. 7. However, askilled artisan will readily recognize that various additionalfunctional components may be supported by the search term expansionmodule 212 to facilitate additional functionality that is notspecifically described herein. Furthermore, the various functionalmodules depicted in FIG. 7 may reside on a single computing device ormay be distributed across several computing devices in variousarrangements such as those used in cloud-based architectures

The search term expansion module 212 also generates an expanded searchterm based on previous search result selections by users. The searchsystem 104 maintains historical search logs that identify the searchterms used in previous search queries, the search results presented as aresult of those previous search queries, as well as data indicating thesearch results that were selected by the users. The search termexpansion module 212 identifies previous search queries that wereexecuted based on one or more of the same keywords that are included inthe search term provided by the user. The search term expansion module212 uses the search results selected by users in response to thoseprevious search queries to identify candidate alternate search termsthat may be used to generate an expanded search term. The search termexpansion module 212 then ranks the candidate alternate search terms,and selects a candidate alternate search term based on the ranking.

As shown, the search term expansion module 212 includes a data gatheringmodule 702, a Siamese model generation module 704, a regression modelgeneration module 706, a candidate selection module 708, a rankingmodule 710, and an expanded search term generation module 712.

The data gathering module 702 gathers historical search logs. Thehistorical search logs include records that identify search terms usedin previous search queries, the search results presented as a result ofthose previous search queries, and the search results that were selectedby the users. The search results include the titles of the searchresults that were presented to users. For example, in systems thatexecute search queries for available jobs, the search results includethe titles of jobs identified as a result of the user's provided searchparameters. The data gathering module 702 gathers the historical searchlogs from the data storage 214.

The Siamese model generation module 704 generates a Siamese model thatdetermines a set of candidate alternate search terms based on an inputsearch term. The Siamese model is a Siamese type deep semanticsimilarity model (DSSM) with different configurations for matchingsearch terms to corresponding keywords in the titles of the searchresults that were previously presented to users. The Siamese modelgeneration module 704 generates the Siamese model based on thehistorical search logs gathered by the data gathering module 702.Specifically, the Siamese model generation module 704 trains the Siamesemodel on search terms and the titles of the corresponding selectedsearch results. The Siamese model generation module 704 trains a pair ofsame networks with parameter sharing. One of the models is trained forthe search terms, and the other model is trained for the titles of thecorresponding selected search results.

The Siamese model includes multiple layers. For example, the Siamesemodel includes an embedding layer, a deep network later, and asimilarity layer. Data input into the Siamese model is processed througheach layer to provide an output. The Siamese model takes as input both asearch term and a title of a clicked search result, and outputs asimilarity score indicating a determined similarity between the searchterm and the title of the clicked search result.

FIG. 8 illustrates a Siamese model 800, accordingly to some example,embodiments. As shown, the Siamese model 800 includes two similarnetworks; a first network 802 for search terms, and a second network 804for the titles of selected search results. The first network 802 takesas input a search term, which includes either a combination of keywordsthat make up the search term, or a subset of the keywords that make upthe search term. The second network 804 takes as input the title of aclicked search result. This includes either a combination of eachkeyword included the title or a subset of the keywords.

As shown, the Siamese model 800 includes 3 layers; an embedding layer806, a deep network layer 808, and a similarity layer 810. Data that isinput into the first network 802 (i.e., search term) and the secondnetwork 804 (i.e., title of a clicked search result) of the Siamesemodel 800 are processed through each of the three layers 806, 808 and810, and result in a single output similarity score. The similarityscore indicates a similarity between the input search term and the inputtitle of a clicked search result. For example, the data inputs are firstprocessed through the embedding layer 806, followed by the deep networklayer 808, and then finally through the similarity layer 810. Althoughthe Siamese model 800 is shown as having three layers 806, 808 and 810,this is only one example and is not meant to be limiting. The Siamesemodel 800 may have any number of layers, and any of the layers (e.g.,the three layers 806, 808 or 810) may have any number of sublayers. Thisdisclosure anticipates all such embodiments. Each of the layers 806, 808and 810 of the Siamese model 800 are discussed in greater detail below.

The embedding layer 806 learns the semantic representation of a datainput. This is accomplished in two steps. In the first step, theincoming text of the input data (i.e., search term and title of searchresult) is converted into multiple n-letter tokens. For example, forassuming that n=3, the embedding layer 806 converts the input text “abc”into the following 3-letter tokens: #ab, abc, and bc#, where # is theboundary token. Hashing the input text into n-letter tokens providesseveral advantages, such as dimensionality reduction and Out ofVocabulary (OOV) word representation.

At the second step, the embedding layer 806 converts the previouslyhashed tokens (i.e., n-letter tokens) into vectors in amulti-dimensional vector space. For example, the embedding layer 806takes the previously hashed tokens and represents them in a300-dimensional vector space. Although the two steps of the embeddinglayer 806 (i.e., hashing the input tokens and converting them intovectors) is described as being a single layer in the Siamese model 800this is just one way of describing the Siamese model 800, and is notmeant to be limiting. The functionality of the embedding layer 806 mayalso be described as two separate layers. For example, a first layer mayinclude hashing the input tokens into multiple n-letter tokens, and thesecond layer may include converting the previously hashed tokens (i.e.,n-letter tokens) into vectors in a multi-dimensional vector space.

The deep network layer 808 may be comprised of multiple neural networklayers. The deep network layer may include fully connected connectionsamong the neural network layers. The deep network layer 808 may use tankas the activation unit. The deep network layer 808 receives as input thevector representations generated by the embedding layer 806, andprovides the output to the similarity later 810.

The similarity layer 810 is a cosine similarity layer. The similaritylayer 810 takes the output of the first network 802 and the secondnetwork 804, which are vector representations of the inputs of eachnetwork 802 and 804, and determines a similarity score indicating asimilarity between the two inputs. In other words, the similarity scoreindicates a similarity between the search term input into the firstnetwork 802 and title input into the second network 804. The similaritylayer 810 determines the similarity score by determining a cosinesimilarity between the two vector representations. Cosine similarity isa measure of similarity between two non-zero vectors of an inner productspace that measures the cosine of the angle between them.

Ultimately, the Siamese network 800 provides a set of candidatealternate search terms for a given search term, as well as similarityscores for each of the search terms. The similarity score for eachcandidate alternate search term indicates a similarity between thesearch term and the respective candidate alternate search term.

Returning to the discussion of FIG. 7, the regression model generationmodule 706 generates a regression model that ranks the set of candidatealternate search terms. The regression model generation module 706trains the regression model based on historical search logs gathered bythe data gathering module 702. Specifically, the regression model istrained on previous search query. reformulations identified in thehistorical search logs. A search query reformulation is a revised searchquery generated by a user during a single session. A session is a periodof time in which a user is performing searches. The regression modelgeneration module 706 analyzes the historical search logs to identifysearch sessions that include two or more consecutive search queries thatare each within a threshold time period of their preceding and followingsearch queries. For example, the regression model generation module 706determines that two consecutive search queries are part of the samesearch session if they occurred within a threshold period of time ofeach other. Alternatively, the regression model generation module 706determines that two consecutive search queries are part of differentsearch session if they occurred outside of a threshold period of time ofeach other. A search session may include any number of search queries,as long as each search query is within the threshold period of time ofthe previous search query in the search session.

The regression model generation module 706 may use other rules indetermining search sessions. For example, the regression modelgeneration module 706 discard a search query if it is an advanced orfacet search query. As another example, the regression model generationmodule 706 may merge two search queries if they are equal (e.g., includethe same search term and geographic indicator), or are determined to beessentially equal. For example, a second search query may include asearch term that is a corrected spelling of a search term included inthe first search query. The regression model generation module 706 maydiscard an identified search session if there are less than 2 searchqueries in the search session, any search query in the search session isa phrase query or a Boolean query, any search query in the searchsession includes an inappropriate term, or any search query in thesearch session includes emails or other personal information.

After identifying the search session, the regression model generationmodule 706 identifies query reformulation pairs in each search sessionand calculates features for each identified query reformulation pair. Aquery reformulation pair is a pair of two search queries in a searchsession. For example, given a search session that includes a set ofsearch queries q₁, q₂, . . . q_(n), each pair (q_(i), q_(j)) isidentified as a query reformulation pair where i<j (i.e., q_(i) occurredchronologically before q_(j)).

For each identified query reformulation pair (q_(i), q_(j)), theregression model generation module 706 computes a set of features. Thefeatures may include the searchers identifier, whether q_(i) wasclicked, whether q_(j)was clicked, whether q_(i) was spelled correctly,whether q_(j) was spelled correctly, spell correction of q_(i), spellcorrection of q_(j), whether q_(i) was followed by a facet view, whetherq_(j) was followed by a facet view, an index of q_(i) in the searchsession, whether q_(j) is the last search query in the search session,the number of search queries that occurred between q_(i) and q_(j), anelapsed time between q_(i) and q_(j), a number of search results fromq_(i), a number of search results from q_(j), etc.

For each identified query reformulation pair (q_(i), q_(j)), theregression model generation module 706 may also compute aggregatedfeatures determined from multiple search sessions. Examples ofaggregates features include a number of occurrences of the queryreformulation pair, number of distinct searchers, a chi-square testscore, number of occurrences in which q_(i) was clicked, number ofoccurrences in which q_(j) was clicked, number of occurrences in whichboth q_(i) and q_(j) were clicked, number of occurrences where q_(j) isabandoned (i.e., q_(j) is the last search query in the search sessionand q_(j) wasn't clicked), the mean/variance of number of search queriesthat occurred between q_(i) and q_(j), the mean/variance of the elapsestime between q_(i) and q_(j), etc.

For each identified query reformulation pair (q_(i), q_(j)), theregression model generation module 706 may also compute query levelfeatures. Examples of query level features include whether a number ofsearch results from q_(j) is less than a threshold number, a relation ofthe number of search results from q_(i) versus a number of searchresults from q_(j), whether q_(j) is a relaxation of q_(i) (broadeningscope of q_(i)), whether q_(j) is a restriction of q_(i) (narrowing ofq_(i)), etc.

The regression model generation module 706 trains the regression modelbased on the identified query reformulation pairs and theircorresponding features. The resulting regression model is used to rankthe set of candidate alternate search terms.

The candidate selection module 708 determines a set of candidatealternate search terms based on a given search term. For example, thecandidate selection module 708 uses the Siamese model generated by theSiamese model generation module 704 to generate the set of candidatealternate search terms. That is, the candidate selection module 708 usesa search term provided by a user as input in the Siamese model toproduce the set of candidate alternate search terms.

The ranking module 710 ranks the set of candidate alternate search ternsusing the regression model generated by the regression model generationmodule 706. For example the ranking module 710 uses the set of candidatealternate search terms as input in the regression module to result in aranking of the set of candidate alternate search terms.

The expanded search term generation module 712 generates an expandedsearch term based on the ranked set of candidate alternate search terms.In some implementations, the expanded search term generation module 712generates the expanded search term by adding one or more new keywords tothe original search term. The added keywords are selected from theranked set of candidate alternate search terms. For example, theexpanded search term generation module 712 selects one or more of thecandidate alternate search terms that are ranked the highest.

In other implementations, the expanded search term generation module 712generates the expanded search term by replacing one or more originalkeywords with one or more of the candidate alternate search terms. Thecandidate alternate search terms are selected from the ranked set ofcandidate alternate search terms. For example, the expanded search termgeneration module 712 selects one or more of the candidate alternatesearch terms that are ranked the highest.

FIG. 9 is a flowchart showing an example method of determining anexpanded search term, according to certain example embodiments. Themethod 900 may be embodied in computer readable instructions forexecution by one or more processors such that the operations of themethod 900 may be performed in part or in whole by the search termexpansion module 212; accordingly, the method 900 is described below byway of example with reference thereto. However, it shall be appreciatedthat at least some of the operations of the method 900 may be deployedon various other hardware configurations and the method 900 is notintended to be limited to the search term expansion module 212.

At operation 902, the keyword identification module 602 identifies atarget company in search parameters. The search parameters are providedby a user to perform a search query. The keyword identification module602 uses a listing of known company names and searches the listing basedon the keywords in the search term to identify a match.

At operation 904, the alternate company selection module 608 identifiesan alternate company based on a set of peer scores. The peer scoresindicate the probability that employees will transition betweencompanies. The alternate company selection module 608 selects analternate company based on the calculated peer scores. For example, thealternate company selection module 608 selects the alternate companywith the highest peer score with the company identified in the searchterm. Alternatively, the alternate company selection module 608 selectsan alternate company that has a peer score that meets or exceeds athreshold peer score. For example, the alternate company selectionmodule 608 identifies all the companies that have a peer score above athreshold peer score and then selects one of the companies as thealternate company. This can be performed using any of a variety oftechniques, such as by random, alphabetical, etc.

At operation 906, the expanded search term generation module 610generates an expanded search term based on the alternate company. Insome implementations, the expanded search term generation module 610generates the expanded search term by adding one or more new keywords tothe original search term. The added keywords identify the alternatecompany. For instance, if the original search term was “SoftwareEngineer Apple,” the expanded search term generation module 610 may addthe alternate company name (e.g. Google) to the search term as a newkeyword. As a result, the expanded search term would comprise “SoftwareEngineer Apple Google.” A search query based on this expanded searchterm would return results for software engineer positions at both Appleand Google, thereby providing a user with additional search results.

In other implementations, the expanded search term generation module 610generates the expanded search term by replacing one or more originalkeywords with one or more new keywords that identify the alternatecompany. For example, given the original search term “Software EngineerApple,” the expanded search term generation module 610 replaces thekeyword ‘Apple’ with a keyword identifying the alternate company (e.g.,Google). As a result, the expanded search term would comprise “SoftwareEngineer Google,” A search query based on this expanded search termwould return results for software engineer positions at Google. Thesesearch results would then be combined with search results based on theoriginal search term of Software Engineer Apple,” thereby providing auser with additional search results for software engineer positions atboth Apple and Google.

FIG. 10 is a flowchart showing an example method of determining anexpanded search term, according to certain example embodiments. Themethod 1000 may be embodied in computer readable instructions forexecution by one or more processors such that the operations of themethod 1000 may be performed in part or in whole by the search termexpansion module 212; accordingly, the method 1000 is described below byway of example with reference thereto. However, it shall be appreciatedthat at least some of the operations of the method 1000 may be deployedon various other hardware configurations and the method 1000 is notintended to be limited to the search term expansion module 212.

At operation 1002, the candidate selection module 708 determines a setof candidate alternate search terms for a search term included in searchparameters. For example, the candidate selection module 708 uses theSiamese model generated by the Siamese model generation module 704 togenerate the set of candidate alternate search terms. That is, thecandidate selection module 708 uses the search term included in thesearch parameters as input in the Siamese model to produce the set ofcandidate alternate search terms.

At operation 1004, the ranking module 710 ranks the set of candidatealternate search terms. The ranking module 710 ranks the set ofcandidate alternate search terns using the regression model generated bythe regression model generation module 706. For example the rankingmodule 710 uses the set of candidate alternate search terms as input inthe regression module to result in a ranking of the set of candidatealternate search terms.

At operation 1006, the expanded search term generation module 712selects a candidate alternate search term based on the ranking. Forexample, the expanded search term generation module 712 selects one ormore candidate alternate search terms with the highest ranking.

At operation, 1008, the expanded search term generation module 712generates an expanded search term based on the selected candidatealternate search term. In some implementations, the expanded search termgeneration module 712 generates the expanded search term by adding theselected candidate alternate search term to the original search term. Inother implementations, the expanded search term generation module 712generates the expanded search term by replacing one or more keywords inthe original search term with one or more of the candidate alternatesearch terms.

FIGS. 11A and 11B are screenshots of search results generated from anexpanded search query, according to some example embodiments. FIG. 11Ashows a search interface 1100 presenting a user with search results 1102and 1104 from an expanded search query. The search interface 1100includes a search input box 1106, which a user has used to enter searchparameters including a search term (i.e., Sales Marketing), and ageographic indictor (i.e., within 25 miles). A search query based on theuser provided search parameters has resulted in no results, as indicatedby a message 1108 presented to the user on the search interface 1100.The search results 1102 and 1104 presented in the search interface 1100are the result of an expanded search query that was executed using anexpanded search term. This is indicated by a second message 1110presented to the user that indicates that the search results are forjobs similar to sales marketing, which was the user's provided searchterm.

FIG. 11B shows the search interface as in FIG. 11A, however in this casethe search results 1102 and 1104 are based on an expanded geographicindicator, rather than an expanded search term. This is indicated by themessage 1112 indicating that the search results 1102 and 1104 are basedon a geographic indicator of within 50 miles, rather than within just 25miles as provided by the user's search parameters.

Software Architecture

FIG. 12 is a block diagram illustrating an example software architecture1206, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 12 is a non-limiting example of asoftware architecture 1206 and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1206 may execute on hardwaresuch as machine 1300 of FIG. 13 that includes, among other things,processors 1304, memory 1314, and (input/output) I/O components 1318. Arepresentative hardware layer 1252 is illustrated and can represent, forexample, the machine 1300 of FIG. 13. The representative hardware layer1252 includes a processing unit 1254 having associated executableinstructions 1204. Executable instructions 1204 represent the executableinstructions of the software architecture 1206, including implementationof the methods, components, and so forth described herein. The hardwarelayer 1252 also includes memory and/or storage modules memory/storage1256, which also have executable instructions 1204. The hardware layer1252 may also comprise other hardware 1258.

In the example architecture of FIG. 12, the software architecture 1206may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1206may include layers such as an operating system 1202, libraries 1220,frameworks/middleware 1218, applications 1216, and a presentation layer1214. Operationally, the applications 1216 and/or other componentswithin the layers may invoke API calls 1208 through the software stackand receive a response such as messages 1212 in response to the APIcalls 1208. The layers illustrated are representative in nature and notall software architectures have all layers. For example, some mobile orspecial purpose operating systems may not provide aframeworks/middleware 1218, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1202 may manage hardware resources and providecommon services. The operating system 1202 may include, for example, akernel 1222, services 1224, and drivers 1226. The kernel 1222 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1222 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1224 may provideother common services for the other software layers. The drivers 1226are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1226 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth, depending on thehardware configuration.

The libraries 1220 provide a common infrastructure that is used by theapplications 1216 and/or other components and/or layers. The libraries1220 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 1202 functionality (e.g., kernel 1222,services 1224 and/or drivers 1226). The libraries 1220 may includesystem libraries 1244 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1220 may include API libraries 1246 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 1220 may also include a wide variety of otherlibraries 1248 to provide many other APIs to the applications 1216 andother software components/modules.

The frameworks/middleware 1218 (also sometimes referred to asmiddleware) provide a higher-level common infrastructure that may beused by the applications 1216 and/or other software components/modules.For example, the frameworks/middleware 1218 may provide various graphicuser interface (GUI) functions, high-level resource management,high-level location services, and so forth. The frameworks/middleware1218 may provide a broad spectrum of other APIs that may be used by theapplications 1216 and/or other software components/modules, some ofwhich may be specific to a particular operating system 1202 or platform.

The applications 1216 include built-in applications 1238 and/orthird-party applications 1240. Examples of representative built-inapplications 1238 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 1240 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1240 may invoke the API calls 1208 provided bythe mobile operating system (such as operating system 1202) tofacilitate functionality described herein.

The applications 1216 may use built in operating system functions (e.g.,kernel 1222, services 1224 and/or drivers 1226), libraries 1220, andframeworks/middleware 1218 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such aspresentation layer 1214. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 13 is a block diagram illustrating components of a machine 1300,according to some example embodiments, able to read instructions 1204from a machine-readable medium (e.g., a machine-readable storage medium)and perform any one or more of the methodologies discussed herein.Specifically, FIG. 13 shows a diagrammatic representation of the machine1300 in the example form of a computer system, within which instructions1310 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1300 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1310 may be used to implement modules or componentsdescribed herein. The instructions 1310 transform the general,non-programmed machine 1300 into a particular machine 1300 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1300 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1300 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1300 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e,g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine 1300 capable of executing theinstructions 1310, sequentially or otherwise, that specify actions to betaken by machine 1300. Further, while only a single machine 1300 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1310 to perform any one or more of the methodologiesdiscussed herein.

The machine 1300 may include processors 1304, memory/storage 1306, andI/O components 1318, which may be configured to communicate with eachother such as via a bus 1302. The memory/storage 1306 may include amemory 1314, such as a main memory, or other memory storage, and astorage unit 1316, both accessible to the processors 1304 such as viathe bus 1302. The storage unit 1316 and memory 1314 store theinstructions 1310 embodying any one or more of the methodologies orfunctions described herein. The instructions 1310 may also reside,completely or partially, within the memory 1314, within the storage unit1316, within at least one of the processors 1304 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1300. Accordingly, the memory 1314, thestorage unit 1316, and the memory of processors 1304 are examples ofmachine-readable media.

The I/O components 1318 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1318 that are included in a particular machine 1300 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1318 may include many other components that are not shown inFIG. 13. The I/O components 1318 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1318may include output components 1326 and input components 1328. The outputcomponents 1326 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1328 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1318 may includebiometric components 1330, motion components 1334, environmentalcomponents 1336, or position components 1338 among a wide array of othercomponents. For example, the biometric components 1330 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1334 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1336 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components one or more microphones thatdetect background noise), proximity sensor components infrared sensorsthat detect nearby objects), gas sensors (e.g., gas detection sensors todetect concentrations of hazardous gases for safety or to measurepollutants in the atmosphere), or other components that may provideindications, measurements, or signals corresponding to a surroundingphysical environment. The position components 1338 may include locationsensor components (e.g., a GPS receiver component), altitude sensorcomponents (e.g., altimeters or barometers that detect air pressure fromwhich altitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1318 may include communication components 1340operable to couple the machine 1300 to a network 1332 or devices 1320via coupling 1324 and coupling 1322, respectively. For example, thecommunication components 1340 may include a network interface componentor other suitable device to interface with the network 1332. In furtherexamples, communication components 1340 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, near field communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1320 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1340 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1340 may include radio frequency identification(REID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1340, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1310 forexecution by the machine 1300, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such instructions 1310. Instructions 1310 may betransmitted or received over the network 1332 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1300 thatinterfaces to a communications network 1332 to obtain resources from oneor more server systems or other client devices. A client device may be,but is not limited to, a mobile phone, desktop computer, laptop, PDAs,smart phones, tablets, ultra books, netbooks, laptops, multi-processorsystems, microprocessor-based or programmable consumer electronics, gameconsoles, STBs, or any other communication device that a user may use toaccess a network 1332.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1332 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network1332 or a portion of a network 1332 may include a wireless or cellularnetwork and the coupling may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or other type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Cartier Radio Transmission Technology(1xRTT), Evolution-Data. Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard settingorganizations, other long range protocols, or other data transfertechnology.

“MACHINE-READABLE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions 1310 and datatemporarily or permanently and may include, but is not be limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EEPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1310. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1310 (e.g., code) forexecution by a machine 1300, such that the instructions 1310, whenexecuted by one or more processors 1304 of the machine 1300, cause themachine 1300 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors 1304) may be configured by software (e.g., anapplication 1216 or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor 1304 or other programmable processor 1304.Once configured by such software, hardware components become specificmachines 1300 (or specific components of a machine 1300) uniquelytailored to perform the configured functions and are no longergeneral-purpose processors 1304. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component”(or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor 1304 configured by software to become aspecial-purpose processor, the general-purpose processor 1304 may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors 1304, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses 1302) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors 1304that are temporarily configured (e.g., by software) or permanentlyconfigured to perform the relevant operations. Whether temporarily orpermanently configured, such processors 1304 may constituteprocessor-implemented components that operate to perform one or moreoperations or functions described herein. As used herein,“processor-implemented component” refers to a hardware componentimplemented using one or more processors 1304. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors 1304 being an example of hardware.For example, at least some of the operations of a method may beperformed by one or more processors 1304 or processor-implementedcomponents. Moreover, the one or more processors 1304 may also operateto support performance of the relevant operations in a “cloud computing”environment or as a “software as a service” (SaaS). For example, atleast some of the operations may be performed by a group of computers(as examples of machines 1300 including processors 1304), with theseoperations being accessible via a network 1332 (e,g., the Internet) andvia one or more appropriate interfaces (e.g., an API). The performanceof certain of the operations may be distributed among the processors1304, not only residing within a single machine 1300, but deployedacross a number of machines 1300. In some example embodiments, theprocessors 1304 or processor-implemented components may be located in asingle geographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, theprocessors 1304 or processor-implemented components may be distributedacross a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1300.A processor 1304 may be, for example, a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC) or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors 1304 (sometimes referred to as “cores”) that may executeinstructions 1310 contemporaneously.

What is claimed is:
 1. A method comprising: determining a set ofcandidate alternate search terms for a search term received from aclient device, the set of candidate alternate search terms determinedbased on historical search logs that include records of previouslysubmitted search terms, corresponding search results that were presentedto users, and corresponding search results that were selected by theusers, the set of candidate alternate search terms being selected fromtitles of the corresponding search results that were selected by theusers; ranking the set of candidate alternate search terms based ondetermined probabilities that each of the alternate candidate searchterms will be selected if presented to a user; selecting, based on theranking, a first candidate alternate search term from the set ofcandidate alternate search terms; generating an expanded search termbased on the first candidate alternate search term; executing a searchquery based on the expanded search term, yielding expanded searchresults; and causing presentation of the expanded search results on theclient device.
 2. The method of claim 1, wherein determining the set ofcandidate alternate search terms comprises: using, as input in a Siamesenetwork based retrieval model, the search term and the titles of thecorresponding search results that were selected by the users, whereinfor each combination of the search term and a title of a correspondingsearch result that was selected by the users, the Siamese network basedretrieval model outputs a similarity score indicating a determinedsimilarity between the search term and the respective title of acorresponding search result that was selected by the users.
 3. Themethod of claim 2, wherein the Siamese network based retrieval modelincludes a first layer, in which the input into the Siamese networkbased retrieval model is converted into a set of fixed character settokens.
 4. The method of claim 3, wherein the Siamese network basedretrieval model includes a second layer, in which a multi-dimensionalvector is generated for each fixed character token from the set of fixedcharacter set tokens.
 5. The method of claim 4, wherein the Siamesenetwork based retrieval model includes a third layer that is a deepneural network that receives the multi-dimensional vectors generated foreach fixed character token from the set of fixed character set tokens asinput.
 6. The method of claim 5, wherein the Siamese network basedretrieval model includes a fourth layer that calculates, based on anoutput of the deep neural network, the similarity score indicating thedetermined similarity between the search term and the respective titlethat were used as input in the Siamese network based retrieval model. 7.The method of claim 1, wherein ranking the set of candidate alternatesearch terms comprises: using the set of candidate alternate searchterms as input in a logistic regression model that was trained based ona subset of the historical search logs determined to include records ofsearch query reformulation by users, the logistic regression modeloutputting the ranking of the set of candidate alternate search terms.8. A system comprising: one or more computer processors; and one or morecomputer-readable mediums storing instructions that, when executed bythe one or more computer processors, cause the system to performoperations comprising: determining a set of candidate alternate searchterms for a search term received from a client device, the set ofcandidate alternate search terms determined based on historical searchlogs that include records of previously submitted search terms,corresponding search results that were presented to users, andcorresponding search results that were selected by the users, the set ofcandidate alternate search terms being selected from titles of thecorresponding search results that were selected by the users; rankingthe set of candidate alternate search terms based on determinedprobabilities that each of the alternate candidate search terms will beselected if presented to a user; selecting, based on the ranking, afirst candidate alternate search term from the set of candidatealternate search terms; generating an expanded search term based on thefirst candidate alternate search term; executing a search query based onthe expanded search term, yielding expanded search results; and causingpresentation of the expanded search results on the client device.
 9. Thesystem of claim 8, wherein determining the set of candidate alternatesearch terms comprises: using, as input in a Siamese network basedretrieval model, the search term and the titles of the correspondingsearch results that were selected by the users, wherein for eachcombination of the search term and a title of a corresponding searchresult that was selected by the users, the Siamese network basedretrieval model outputs a similarity score indicating a determinedsimilarity between the search term and the respective title of acorresponding search result that was selected by the users.
 10. Thesystem of claim 8, wherein the Siamese network based retrieval modelincludes a first layer, in which the input into the Siamese networkbased retrieval model is converted into a set of fixed character settokens.
 11. The system of claim 10, wherein the Siamese network basedretrieval model includes a second layer, in which a multi-dimensionalvector is generated for each fixed character token from the set of fixedcharacter set tokens.
 12. The system of claim 11, wherein the Siamesenetwork based retrieval model includes a third layer that is a deepneural network that receives the multi-dimensional vectors generated foreach fixed character token from the set of fixed character set tokens asinput.
 13. The system of claim 12, wherein the Siamese network basedretrieval model includes a fourth layer that calculates, based on anoutput of the deep neural network, the similarity score indicating thedetermined similarity between the search term and the respective titlethat were used as input in the Siamese network based retrieval model.14. The system of claim 8, wherein ranking the set of candidatealternate search terms comprises: using the set of candidate alternatesearch terms as input in a logistic regression model that was trainedbased on a subset of the historical search logs determined to includerecords of search query reformulation by users, the logistic regressionmodel outputting the ranking of the set of candidate alternate searchterms.
 15. A non-transitory computer-readable medium storinginstructions that, when executed by one or more computer processors of acomputing system, cause the computing system to perform operationscomprising: determining a set of candidate alternate search terms for asearch term received from a client device, the set of candidatealternate search terms determined based on historical search logs thatinclude records of previously submitted search terms, correspondingsearch results that were presented to users, and corresponding searchresults that were selected by the users, the set of candidate alternatesearch terms being selected from titles of the corresponding searchresults that were selected by the users; ranking the set of candidatealternate search terms based on determined probabilities that each ofthe alternate candidate search terms will be selected if presented to auser; selecting, based on the ranking, a first candidate alternatesearch term from the set of candidate alternate search terms; generatingan expanded search term based on the first candidate alternate searchterm; executing a search query based on the expanded search term,yielding expanded search results; and causing presentation of theexpanded search results on the client device.
 16. The non-transitorycomputer-readable medium of claim 15, wherein determining the set ofcandidate alternate search terms comprises: using, as input in a Siamesenetwork based retrieval model, the search term and the titles of thecorresponding search results that were selected by the users, whereinfor each combination of the search term and a title of a correspondingsearch result that was selected by the users, the Siamese network basedretrieval model outputs a similarity score indicating a determinedsimilarity between the search term and the respective title of acorresponding search result that was selected by the users.
 17. Thenon-transitory computer-readable medium of claim 15, wherein the Siamesenetwork based retrieval model includes a first layer, in which the inputinto the Siamese network based retrieval model is converted into a setof fixed character set tokens.
 18. The non-transitory computer-readablemedium of claim 17, wherein the Siamese network based retrieval modelincludes a second layer, in which a multi-dimensional vector isgenerated for each fixed character token from the set of fixed characterset tokens.
 19. The non-transitory computer-readable medium of claim 18,wherein the Siamese network based retrieval model includes a third layerthat is a deep neural network that receives the multi-dimensionalvectors generated for each fixed character token from the set of fixedcharacter set tokens as input.
 20. The non-transitory computer-readablemedium of claim 19, wherein the Siamese network based retrieval modelincludes a fourth layer that calculates, based on an output of the deepneural network, the similarity score indicating the determinedsimilarity between the search term and the respective title that wereused as input in the Siamese network based retrieval model.